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Category: Commentary

Chronocentrism in the Social Sciences

A few weeks ago, I discussed the dangers of chronocentrism, which has been defined by British science journalist Tom Standage as “the egotism that one’s own generation is poised on the very cusp of history.” I wrote:

From Fukuyama’s The End of History and the Last Man — “History is directional, and its endpoint is capitalist liberal democracy” — to Rifkin’s The End of Work — “We are entering a new phase in history, one characterized by the steady and inevitable decline of jobs” — to Millenarians, present-biasedness and the belief that the old rules no longer apply seem insuperable for many people.

Nowhere was this clearer than when the world’s population hit seven billion a few weeks ago. Never mind the past 25 million years of human evolution, during which humans always managed to develop technologies to feed themselves. Never mind the fact that famines are man-made and not directly caused by a lack of food to go around. Never mind all that: many commentators saw fit to inform us that the old rules no longer applied, and that we were about to enter an era of starvation and famine.

Chronocentrism in the Social Sciences

Chronocentrism is particularly pernicious in the social sciences in general and in economics in particular.

Indeed, for many, it seems difficult to take a long view of the history of economic thought and admit that, much as we chuckle at some of the research “findings” of a few hundred years ago (Malthus is a particularly good example), researchers 50 years from now will find plenty to criticize about our own work — if they read it at all. Yet how many times do we seem willing to fight tooth and nail to defend research findings?

I get the impression that in the natural sciences, researchers are more aware of this particular manifestation of present-biasedness. In the natural sciences, the understanding that no paper is perfect appears widespread, and researchers seem conscious that their work can be improved.

Perhaps as a result, researchers in the natural sciences cite each other’s work a lot more than economists and other social scientists do.

“Vous êtes pas tannés de mourir, bande de caves?”*

When I started blogging at the end of 2010, I swore never to discuss Québec politics in this space, first and foremost because my expertise lies in development policy, but also because my opinions about Québec politics, society, and culture are not shared by many.

I thus thought I would spare myself being called a sellout — not only do I live and work in the US, I also write in English, two things that are often viewed with suspicion in Québec — and keep my political opinions to myself.

Earlier this week, however, Jérôme Lussier wrote a column titled “Doléances pour un Québec dépassé” (“Complaints for an outdated Québec”) in Voir, a Montreal-based independent weekly, and every single thing he wrote deserves to be said, repeated, and broadcast far and wide. Lussier’s column is a deeply humanistic cri du coeur.

Because it’s the holidays, I am in a giving mood. So here is my bit of agitprop for the year: my translation of Lussier’s column. Bear in mind that the following is the intellectual property of Lussier.

There’s No Free (Causality) Lunch

First of all, causality requires identification. Vector autoregressions (VARs) do not provide any automatic or free identification. To do policy analysis with a VAR (as opposed to agnostic forecasting) one has to make the same type of untestable identifying assumptions here as one does in the older, explicitly simultaneous equation, Cowles commission approach.

The most common way of identifying a VAR (ordering the variables and performing a Cholesky decomposition) is EXACTLY the same as using exclusion restrictions to identify a system of equations. Other structural VARS do NOT remove the need for identifying assumptions. VARS are not a free lunch.

That is Kevin Grier, in a post over at Kids Prefer Cheese.

I was looking forward to someone finally saying it, as many of the media discussions of the 2011 Nobel prize for economics somehow made it sound as though the work of Sargent and Sims allowed us to estimate causal relationships.

Not so. The estimation of causal relationships is difficult in the social sciences for the simple reason that we almost never observe the counterfactual. This is especially true in macroeconomics, where the opportunities to run an experiment are few and far between. And even in more micro contexts, we’re not completely sure about much.